Point patterns of forest fire locations |
| |
Authors: | Rolf Turner |
| |
Institution: | (1) Department of Mathematics and Statistics, University of New Brunswick, Fredericton, Canada |
| |
Abstract: | In this paper I demonstrate some of the techniques for the analysis of spatial point patterns that have become available due
to recent developments in point process modelling software. These developments permit convenient exploratory data analysis,
model fitting, and model assessment. Efficient model fitting, in particular, makes possible the testing of statistical hypotheses
of genuine interest, even when interaction between points is present, via Monte Carlo methods. The discussion of these techniques
is conducted jointly with and in the context of some preliminary analyses of a collection of data sets which are of considerable
interest in their own right. These data sets (which were kindly provided to me by the New Brunswick Department of Natural
Resources) consist of the complete records of wildfires which occurred in New Brunswick during the years 1987 through 2003.
In treating these data sets I deal with data-cleaning problems, methods of exploratory data analysis, means of detecting interaction,
fitting of statistical models, and residual analysis and diagnostics. In addition to demonstrating modelling techniques, I
include a discussion on the nature of statistical models for point patterns. This is given with a view to providing an understanding
of why, in particular, the Strauss model fails as a model for interpoint attraction and how it has been modified to overcome
this difficulty. All actual modelling of the New Brunswick fire data is done only with the intent of illustrating techniques.
No substantive conclusions are or can be drawn at this stage. Realistic modelling of these data sets would require incorporation
of covariate information which I do not so far have available.
|
| |
Keywords: | Covariates Critical envelope Geyer model K-function Kernel smoothing Inhomogeneous K-function Intensity Interaction Observation window Papangelou conditional intensity function Strauss model |
本文献已被 SpringerLink 等数据库收录! |
|